Classifying Temporomandibular Disorder with Artificial Intelligent Architecture Using Magnetic Resonance Imaging

Zih Kai Kao, Neng Tai Chiu, Hung Ta Hondar Wu, Wan Chen Chang, Ding-Han Wang, Yen Ying Kung, Pei Chi Tu, Wen Liang Lo, Yu-Te Wu*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

This study proposes a new diagnostic tool for automatically extracting discriminative features and detecting temporomandibular joint disc displacement (TMJDD) accurately with artificial intelligence. We analyzed the structural magnetic resonance imaging (MRI) images of 52 patients with TMJDD and 32 healthy controls. The data were split into training and test sets, and only the training sets were used for model construction. U-net was trained with 100 sagittal MRI images of the TMJ to detect the joint cavity between the temporal bone and the mandibular condyle, which was used as the region of interest, and classify the images into binary categories using four convolutional neural networks: InceptionResNetV2, InceptionV3, DenseNet169, and VGG16. The best models were InceptionV3 and DenseNet169; the results of InceptionV3 for recall, precision, accuracy, and F1 score were 1, 0.81, 0.85, and 0.9, respectively, and the corresponding results of DenseNet169 were 0.92, 0.86, 0.85, and 0.89, respectively. Automated detection of TMJDD from sagittal MRI images is a promising technique that involves using deep learning neural networks. It can be used to support clinicians in diagnosing patients as having TMJDD.

Original languageEnglish
JournalAnnals of Biomedical Engineering
DOIs
StateAccepted/In press - 2022

Keywords

  • Deep learning
  • Diagnosis, computer-assisted
  • Image interpretation, computer-assisted
  • Pattern recognition, automated
  • Spatial analysis
  • Temporomandibular joint disc

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